\documentclass{article}
\usepackage[ae,hyper]{Rd}
\begin{document}
\HeaderA{rule}{Rule}{rule}
\begin{Description}\relax
A single rule generated from a decision tree.
\end{Description}
\begin{Usage}
\begin{verbatim}data(rule)\end{verbatim}
\end{Usage}
\begin{Format}\relax
The format is:
NULL
\end{Format}

\HeaderA{merge.rulesets}{Merge Rule Sets}{merge.rulesets}
\keyword{merge}{merge.rulesets}
\begin{Description}\relax
Merges two rule sets into one.
\end{Description}
\begin{Usage}
\begin{verbatim}
merge.rulesets(ruleset1, ruleset2)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{ruleset1}] first rule set to merge.
\item[\code{ruleset2}] second rule set to merge.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The function returns new merged rule set.
\end{Details}

\HeaderA{bootstrap}{Bootstrap Data}{bootstrap}
\begin{Description}\relax
Bootstrap given data.
\end{Description}
\begin{Usage}
\begin{verbatim}
bootstrap(data, n)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{data}] a data set to bootstrap.
\item[\code{n}] number of randomly chosen examples from the given dataset to return.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The function returns bootstrapped data set as a data frame. The function uses \code{sample}   function to randomize results.
\end{Details}
\begin{Examples}
\begin{ExampleCode}
## bootstrap iris data set, take 10 examples
bootstrap(iris, 10)
\end{ExampleCode}
\end{Examples}

\HeaderA{contingencyTable}{Contingency Table For A Rule}{contingencyTable}
\keyword{contingency}{contingencyTable}
\begin{Description}\relax
Calculates contingency table for a rule.
\end{Description}
\begin{Usage}
\begin{verbatim}
contingencyTable(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate contingency table for.
\item[\code{data}] a data set used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{personKsi2}{Pearson ksi2 Statistic}{personKsi2}
\begin{Description}\relax
Calculates Pearson ksi2 Statistic.
\end{Description}
\begin{Usage}
\begin{verbatim}
personKsi2(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to test.
\item[\code{data}] a data set used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{qCohen}{Cohens Formula}{qCohen}
\begin{Description}\relax
Calculates Cohens Formula
\end{Description}
\begin{Usage}
\begin{verbatim}
qCohen(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate measure for.
\item[\code{data}] a data used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{classQual}{Quality of A Classification}{classQual}
\keyword{quality}{classQual}
\keyword{classification}{classQual}
\begin{Description}\relax
Calsulates the quality of a classification using a result data frame from \code{predict} function.
\end{Description}
\begin{Usage}
\begin{verbatim}
classQual(dataFrame, dataset)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{dataFrame}] a result data frame from \code{predict} function.
\item[\code{dataset}] a data frame to test
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The function returns the quality of a classification. It would be a number between 0 and max number of rows of dataset argument. The function simply sums up the probability of assigning an example to the class that an example should be assigned.
\end{Details}

\HeaderA{loadData}{Load qruleset Data}{loadData}
\begin{Description}\relax
Loads data sets used in \code{demo} function.
\end{Description}
\begin{Usage}
\begin{verbatim}
loadData()
\end{verbatim}
\end{Usage}

\HeaderA{classesVector}{Get Data Set Classes}{classesVector}
\keyword{classes}{classesVector}
\begin{Description}\relax
Get vector of unique classes from a given dataset.
\end{Description}
\begin{Usage}
\begin{verbatim}
classesVector(dataset)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{dataset}] a data frame to extract classes from.
\end{ldescription}
\end{Arguments}
\begin{Examples}
\begin{ExampleCode}
## get all classes from iris data set
classesVector(iris)
\end{ExampleCode}
\end{Examples}

\HeaderA{initialize}{Initialize qruleset}{initialize}
\begin{Description}\relax
Initialize the \code{qruleset} package. Loads required packages and sets the path.
\end{Description}
\begin{Usage}
\begin{verbatim}
initialize()
\end{verbatim}
\end{Usage}

\HeaderA{cover}{Coverage For A Rule}{cover}
\keyword{coverage}{cover}
\begin{Description}\relax
Calculates coverage for a rule.
\end{Description}
\begin{Usage}
\begin{verbatim}
cover(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate coverage for.
\item[\code{data}] a data set used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{predict.ruleset}{~~function to do ... ~~}{predict.ruleset}
\begin{Description}\relax
Creates predict table for a given rule set.
\end{Description}
\begin{Usage}
\begin{verbatim}
predict.ruleset(ruleset, dataset, names)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{ruleset}] a given rule set.
\item[\code{dataset}] a data set used in calculating predict table.
\item[\code{names}] a vector of class names.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Similary to predict for \code{rpart} package the function returns predict table.
\end{Details}

\HeaderA{rule.evaluate}{Evaluate A Rule}{rule.evaluate}
\begin{Description}\relax
Evaluates a rule.
\end{Description}
\begin{Usage}
\begin{verbatim}
rule.evaluate(rule, instance)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to evaluate.
\item[\code{instance}] an example from data set.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
~~ If necessary, more details than the description above ~~
\end{Details}

\HeaderA{qws}{Consistency and Coverage Weight Sum}{qws}
\begin{Description}\relax
Weight Sum of Consistency and Coverage.
\end{Description}
\begin{Usage}
\begin{verbatim}
qws(rule, data, w1, w2)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate measure for.
\item[\code{data}] a data used in calculation.
\item[\code{w1}] a weight of consistency.
\item[\code{w2}] a weight of coverage (usually smaller).
\end{ldescription}
\end{Arguments}

\HeaderA{cons}{Consistency For A Rule}{cons}
\keyword{consistency}{cons}
\begin{Description}\relax
Calculates consistency for a rule.
\end{Description}
\begin{Usage}
\begin{verbatim}
cons(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate consistancy for.
\item[\code{data}] a data set used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{ruleset.rpart}{Generate Rule Set}{ruleset.rpart}
\begin{Description}\relax
Generate Rule Set using decision tree generated using \code{rpart} package.
\end{Description}
\begin{Usage}
\begin{verbatim}
ruleset.rpart(model)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{model}] a \code{rpart} decision tree.
\end{ldescription}
\end{Arguments}

\HeaderA{demo}{qruleset Demo}{demo}
\begin{Description}\relax
Demo function for \code{qruleset} package.
\end{Description}
\begin{Usage}
\begin{verbatim}
demo()
\end{verbatim}
\end{Usage}

\HeaderA{ruleset}{Rule Set}{ruleset}
\begin{Description}\relax
A Rule Set generated from a decision tree.
\end{Description}
\begin{Usage}
\begin{verbatim}data(ruleset)\end{verbatim}
\end{Usage}
\begin{Format}\relax
The format is:
list()
\end{Format}

\HeaderA{cutRS}{Cut Rule Set}{cutRS}
\begin{Description}\relax
Cuts the worst rules in a given rule set.
\end{Description}
\begin{Usage}
\begin{verbatim}
cutRS(ruleSet, data, fun, rulesToCut)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{ruleSet}] a rule set to cut.
\item[\code{data}] train data used to cut a given rule set.
\item[\code{fun}] quality measure function.
\item[\code{rulesToCut}] number of rules to cut from a given rule set.
\end{ldescription}
\end{Arguments}

\HeaderA{qProd}{Consistency and Coverage Product}{qProd}
\begin{Description}\relax
Product of Consistency and Coverage.
\end{Description}
\begin{Usage}
\begin{verbatim}
qProd(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate measure for.
\item[\code{data}] a data used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{createTree}{Create A Decision Tree}{createTree}
\keyword{tree}{createTree}
\begin{Description}\relax
Creates a decision tree from a given data set.
\end{Description}
\begin{Usage}
\begin{verbatim}
createTree(x, cpv)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] a data frame with data.
\item[\code{cpv}] complexity parameter value. Should be between 0 and 1.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The function is a wrapper for \code{rpart} from the \code{rpart} package.
\end{Details}

\HeaderA{parse.rule}{Parse A Rule}{parse.rule}
\begin{Description}\relax
Parses a rule.
\end{Description}
\begin{Usage}
\begin{verbatim}
parse.rule(rule)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to parse.
\end{ldescription}
\end{Arguments}

\HeaderA{qColman}{Colmans Formula}{qColman}
\begin{Description}\relax
Calculates Colmans Formula.
\end{Description}
\begin{Usage}
\begin{verbatim}
qColman(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate measure for.
\item[\code{data}] a data used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{qMD}{Disrimination Measure}{qMD}
\begin{Description}\relax
Calculates Measure of Disrimination.
\end{Description}
\begin{Usage}
\begin{verbatim}
qMD(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate measure for.
\item[\code{data}] a data used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{removeNA}{Remove NA Rows}{removeNA}
\begin{Description}\relax
Deals with NA (missing values) by removing rows with NA.
\end{Description}
\begin{Usage}
\begin{verbatim}
removeNA(dataset)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{dataset}] a data set to prepare.
\end{ldescription}
\end{Arguments}

\HeaderA{qC1}{C1 Measure}{qC1}
\begin{Description}\relax
Calculate modified C1 Colmans formula.
\end{Description}
\begin{Usage}
\begin{verbatim}
qC1(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate measure for.
\item[\code{data}] a data used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{personKsi2.ksi2}{Ksi2}{personKsi2.ksi2}
\begin{Description}\relax
Calculates ksi2 for element in contingency table.
\end{Description}
\begin{Usage}
\begin{verbatim}
personKsi2.ksi2(n0, ne)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{n0}] number of examples in cell.
\item[\code{ne}] estimated number of examples.
\end{ldescription}
\end{Arguments}

\HeaderA{qLS}{Logical Sufficiency Measure}{qLS}
\begin{Description}\relax
Calculates Measure of Logical Sufficiency.
\end{Description}
\begin{Usage}
\begin{verbatim}
qLS(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate measure for.
\item[\code{data}] a data used in calculation.
\end{ldescription}
\end{Arguments}

\HeaderA{qruleset-package}{MOW Coursework, dr inz. Pawel Cichosz (supervisor), Warsaw University of Technology.}{qruleset.Rdash.package}
\aliasA{qruleset}{qruleset-package}{qruleset}
\keyword{ruleset}{qruleset-package}
\keyword{quality}{qruleset-package}
\begin{Description}\relax
Coursework project (number 6.) Filtring a rule set generated using package rpart generated decision trees in order to find the best rule subset using various rule quality measure techniques.
\end{Description}
\begin{Details}\relax
\Tabular{ll}{
Package: & qruleset\\
Type: & Package\\
Version: & 1.0\\
Date: & 2009-01-25\\
License: & What license is it under?\\
LazyLoad: & yes\\
}
To run experiments simply type demo(). After typing initialize() you can also use packed functions
\end{Details}
\begin{Author}\relax
Michal Lisiecki, Michal Plutecki, Warsaw University of Technology.

Maintainer: Michal Lisiecki, Michal Plutecki, Warsaw University of Technology.
\end{Author}

\HeaderA{cutRSAndMeasurePredictQual}{Cut Rule Set And Measures The Quality}{cutRSAndMeasurePredictQual}
\begin{Description}\relax
Cuts rule set and measures the quality of predict table.
\end{Description}
\begin{Usage}
\begin{verbatim}
cutRSAndMeasurePredictQual(ruleSet, testData, trainData, classes, name, rulesToCut)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{ruleSet}] a given rule set.
\item[\code{testData}] a test data used in final quality test.
\item[\code{trainData}] a train data used in cutting a rule set.
\item[\code{classes}] all possible classes of the data set.
\item[\code{name}] name of data set.
\item[\code{rulesToCut}] rate indicating number of rules to cut.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Multi-task function used to automate the process of measuring the quality for different data sets.
\end{Details}

\HeaderA{qIS}{Information Score}{qIS}
\begin{Description}\relax
Calculates Information Score.
\end{Description}
\begin{Usage}
\begin{verbatim}
qIS(rule, data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{rule}] a rule to calculate measure for.
\item[\code{data}] a data used in calculation.
\end{ldescription}
\end{Arguments}

\end{document}
